PCA: Progressive class-wise attention for skin lesions diagnosis

Skin cancer is the most prevalent type of cancer worldwide. Early detection is essential as it could be fatal at later stages. The classification of skin lesions is challenging since there are many variations, including changes in color, shape, size, high intra-class variation, and high inter-class similarity. In this paper, a unique class-wise attention method is proposed that considers each class equally while extracting additional discriminative information of skin lesions. The proposed attention mechanism is employed in a progressive manner to incorporate discriminative feature information from multiple scales. The proposed approach obtained competitive performance against more than 15 state-of-the-art methods including HAM1000 and ISIC 2019 leaderboard winners. The proposed method achieved 97.40% accuracy on the HAM10000 and 94.9% accuracy on the ISIC 2019 dataset.